Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the minimal annotations, the description discloses important runtime behavior: it runs asynchronously, returns a generation_id, requires polling, and costs $0.40 per 100 characters. This adds meaningful operational context without contradicting the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three short sentences convey purpose, cost, async behavior, and next-step polling with zero filler. The most important operational detail (polling via get_generation) is present without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For an asynchronous tool with no output schema, the description covers the essential flow: invocation, returned generation_id, and how to obtain the result. It omits polling interval and error cases, but these are moderate gaps given the clarity of the core contract.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already documents all parameters with 100% coverage, so the baseline applies. The description adds no parameter-level semantics beyond cost scaling by character count, which is modest supplemental context.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: making photos talk. It conveys a specific verb/resource pairing and includes the async contract, but it does not explicitly differentiate itself from similar siblings like ai_lip_sync or ai_voice_cloner.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives a clear operational instruction — call get_generation until status is 'completed' — which is valuable guidance. However, it does not state when to choose this tool over alternatives, nor does it mention exclusions or prerequisites.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.